# hybridgroup/gocv

Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.

Repository: https://github.com/hybridgroup/gocv
Canonical: https://ross.abutalabs.com/products/gocv
Homepage: https://gocv.io
Language: Go
License: NOASSERTION
License Family: other
Topics: opencv, golang, video, computer-vision, video-capture, face-tracking, mjpeg, mjpeg-stream, image-processing, tensorflow, computervision, openvino, dnn, gocv, object-tracking, object-classification, cuda, onnx, yolo
Last push: 2026-05-28T08:16:03+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 52, longevity 100
- inputs: {"age_days": 3271, "days_push": 97, "days_rel": 240, "gap_med": 88, "n_releases_24m": 6}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7491, forks 901 (observed 2026-08-28T04:10:00.279704+00:00)

## What it is
GoCV is a Go language binding for the OpenCV 4 computer vision library, supporting Linux, macOS, Windows, and Docker. It includes support for DNN deep learning inference, CUDA GPU acceleration, OpenCV Contrib modules, and Intel OpenVINO.

## Use cases
- process images and video with opencv in go
- detect faces in webcam video stream
- run yolo object detection with cuda acceleration
- capture and display video from a camera in a go program
- run deep learning models like tensorflow or onnx in go
- track objects in video using opencv
- build a real-time computer vision application in golang

## When to choose
- you want to use OpenCV from Go with idiomatic bindings
- you need GPU-accelerated vision via CUDA or OpenVINO
- you want to run DNN models (TensorFlow, ONNX, YOLO) inside a Go application
- you need cross-platform video capture and image processing in Go

## When to avoid
- you are not using the Go language
- you need pure-Go image processing without a native OpenCV dependency
- you want Python's full OpenCV ecosystem and tooling

## Facets
- artifact type: library
- maturity: active
- function: computer-vision, image-processing, video-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, machine-learning
- platform: windows, go
- tags: opencv-bindings, cuda, openvino, dnn, yolo, video-capture, face-detection, object-tracking, video, linux, macos, docker, gpu

## Member repositories
- hybridgroup/gocv (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:00.279704+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:37:34.558296+00:00, confidence not recorded.
  - readme: https://github.com/hybridgroup/gocv (fetched 2026-08-28T04:10:00.279704+00:00, sha 2ca0a1021d42)
  - homepage: https://gocv.io (fetched 2026-08-29T08:32:50.220487+00:00, sha 7f697c5f2911)
  - site_page: https://gocv.io/getting-started (fetched 2026-08-29T08:32:50.222864+00:00, sha f4883d954ae2)
- Data as of 2026-08-30T08:39:29.467469+00:00.